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Conventional sleep monitoring is time-consuming, expensive and uncomfortable,requiring a large number of contact sensors to be attached to the patient.Video data is commonly recorded as part of a sleep laboratory assessment. Ifaccurate sleep staging could be achieved solely from video, this would overcomemany of the problems of traditional methods. In this work we use heart rate,breathing rate and activity measures, all derived from a near-infrared videocamera, to perform sleep stage classification. We use a deep transfer learningapproach to overcome data scarcity, by using an existing contact-sensor datasetto learn effective representations from the heart and breathing rate timeseries. Using a dataset of 50 healthy volunteers, we achieve an accuracy of73.4\% and a Cohen's kappa of 0.61 in four-class sleep stage classification,establishing a new state-of-the-art for video-based sleep staging.

Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera